bitrl & cuberl Documentation
Simulation engine for reinforcement learning agents
Loading...
Searching...
No Matches
play Namespace Reference

Functions

dict load_policy (Path filename)
 

Variables

 policy_path = Path('/home/alex/qi3/cuberl/build/examples/rl/rl_example_10/policy.csv')
 
dict policy = load_policy(policy_path)
 
int max_episode_steps = 200
 
str version = 'v0'
 
str env_tag = f"CliffWalking-{version}"
 
 env
 
 state = observation
 
 _
 
bool done = False
 
int total_reward = 0
 
dict action = policy[state]
 
 observation
 
 reward
 
 truncated
 
 info
 
bool is_slippery = False
 

Function Documentation

◆ load_policy()

dict play.load_policy ( Path  filename)

Variable Documentation

◆ _

play._
protected

◆ action

dict play.action = policy[state]

◆ done

bool play.done = False

◆ env

play.env
Initial value:
1= gym.make(id=env_tag,
2 max_episode_steps=max_episode_steps,
3 render_mode="human")

◆ env_tag

str play.env_tag = f"CliffWalking-{version}"

◆ info

play.info

◆ is_slippery

bool play.is_slippery = False

◆ max_episode_steps

int play.max_episode_steps = 200

◆ observation

play.observation

◆ policy

dict play.policy = load_policy(policy_path)

◆ policy_path

play.policy_path = Path('/home/alex/qi3/cuberl/build/examples/rl/rl_example_10/policy.csv')

◆ reward

play.reward

◆ state

play.state = observation

◆ total_reward

int play.total_reward = 0

◆ truncated

play.truncated

◆ version

str play.version = 'v0'